Serverless Federated Learning for UAV Networks: Architecture, Challenges, and Opportunities

by   Yuben Qu, et al.

Unmanned aerial vehicles (UAVs), or say drones, are envisioned to support extensive applications in next-generation wireless networks in both civil and military fields. Empowering UAVs networks intelligence by artificial intelligence (AI) especially machine learning (ML) techniques is inevitable and appealing to enable the aforementioned applications. To solve the problems of traditional cloud-centric ML for UAV networks such as privacy concern, unacceptable latency, and resource burden, a distributed ML technique, i.e., federated learning (FL), has been recently proposed to enable multiple UAVs to collaboratively train ML model without letting out raw data. However, almost all existing FL paradigms are server-based, i.e., a central entity is in charge of ML model aggregation and fusion over the whole network, which could result in the issue of a single point of failure and are inappropriate to UAV networks with both unreliable nodes and links. To address the above issue, in this article, we propose a novel architecture called SELF-UN (SErverLess FL for UAV Networks), which enables FL within UAV networks without a central entity. We also conduct a preliminary simulation study to validate the feasibility and effectiveness of the SELF-UN architecture. Finally, we discuss the main challenges and potential research directions in the SELF-UN.


page 1

page 6


Machine learning for UAV-Based networks

Unmanned aerial vehicles (UAVs) are considered as one of the promising t...

Empowering the Edge Intelligence by Air-Ground Integrated Federated Learning in 6G Networks

Ubiquitous intelligence has been widely recognized as a critical vision ...

WIP: Federated Learning for Routing in Swarm Based Distributed Multi-Hop Networks

Unmanned Aerial Vehicles (UAVs) are a rapidly emerging technology offeri...

Please sign up or login with your details

Forgot password? Click here to reset